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README.md

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----
-annotations_creators:
-- other
-language_creators:
-- other
-language:
-- sv
-- da
-- nb
-license:
-- cc-by-4.0
-multilinguality:
-- translation
-size_categories:
-- unknown
-source_datasets:
-- extended|glue
-- extended|super_glue
-task_categories:
-- text-classification
-task_ids:
-- natural-language-inference
-- semantic-similarity-classification
-- sentiment-classification
-- text-scoring
-pretty_name: overlim
-tags:
-- qa-nli
-- paraphrase-identification
----
-
-# Dataset Card for OverLim
-
-## Dataset Description
-
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-The _OverLim_ dataset contains some of the GLUE and SuperGLUE tasks automatically
-translated to Swedish, Danish, and Norwegian (bokmål), using the OpusMT models
-for MarianMT.
-
-The translation quality was not manually checked and may thus be faulty.
-Results on these datasets should thus be interpreted carefully.
-
-If you want to have an easy script to train and evaluate your models have a look [here](https://github.com/kb-labb/overlim_eval)
-
-
-### Supported Tasks and Leaderboards
-
-The data contains the following tasks from GLUE and SuperGLUE:
-
-- GLUE
-  - `mnli`
-  - `mrpc`
-  - `qnli`
-  - `qqp`
-  - `rte`
-  - `sst`
-  - `stsb`
-  - `wnli`
-- SuperGLUE
-  - `boolq`
-  - `cb`
-  - `copa`
-  - `rte`
-
-### Languages
-
-- Swedish
-- Danish
-- Norwegian (bokmål)
-
-## Dataset Structure
-
-### Data Instances
-
-Every task has their own set of features, but all share an `idx` and `label`.
-
-- GLUE
-  - `mnli`
-    - `premise`, `hypothesis`
-  - `mrpc`
-    - `text_a`, `text_b`
-  - `qnli`
-    - `premise`, `hypothesis`
-  - `qqp`
-    - `text_a`, `text_b`
-  - `sst`
-    - `text`
-  - `stsb`
-    - `text_a`, `text_b`
-  - `wnli`
-    - `premise`, `hypothesis`
-- SuperGLUE
-  - `boolq`
-    - `question`, `passage`
-  - `cb`
-    - `premise`, `hypothesis`
-  - `copa`
-    - `premise`, `choice1`, `choice2`, `question`
-  - `rte`
-    - `premise`, `hypothesis`
-
-### Data Splits
-
-In order to have test-split, we repurpose the original validation-split as
-test-split, and split the training-split into a new training- and
-validation-split, with an 80-20 distribution.
-
-## Dataset Creation
-
-For more information about the individual tasks see (https://gluebenchmark.com) and (https://super.gluebenchmark.com).
-
-### Curation Rationale
-
-Training non-English models is easy, but there is a lack of evaluation datasets to compare their actual performance.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@kb-labb](https://github.com/kb-labb) for adding this dataset.

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